★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★Open research. Global perspectives.
editor@theamericanjournals.comPublisher Home ↗
Engineering and Technology OPEN ACCESS

Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity

Abdullah Al Mamun
Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Ayan Nath
Master’s in computer and information science, International American University
Sonjoy Kumar Dey
McComish Department of Electrical Engineering and Computer Science, South Dakota State University, USA
Paresh Chandra Nath
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Mohibur Rahman
Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USA
Jannatul Ferdous Shorna
College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida
Nafis Anjum
College of Technology and Engineering, Westcliff University, Irvine, CA
DOI Crossref DOI 10.37547/tajet/Volume07Issue03-23
tajet 2025
VOL. 7 / NO. 03 MARCH
FULL TEXT PP. 252-261
DOWNLOAD PDF

View all articles in this issue ↗

VOLUME 7
ISSUE 03
YEAR 2025
PAGES 252-261
RESEARCH OVERVIEW

Abstract

This study presents a novel malware detection framework for cloud infrastructures that harnesses the power of Convolutional Neural Networks (CNNs) to achieve real-time threat identification with superior accuracy and speed. Our approach begins with the collection and meticulous preprocessing of heterogeneous cloud log data, followed by advanced feature engineering to extract meaningful patterns indicative of malicious activity. The CNN model automatically learns hierarchical representations from this high-dimensional data, resulting in a detection system that achieves an accuracy of 98.2%, a precision of 97.5%, a recall of 98.0%, and an F1-score of 97.8%. In addition, the model operates with a low latency of 12 ms, a critical factor for timely threat mitigation in dynamic cloud environments. Comparative analysis against Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest classifiers reveals that the CNN not only outperforms these models in key performance metrics but also maintains a significant advantage in processing speed. These findings highlight the potential of CNN-based approaches to enhance cybersecurity defenses, offering a scalable and efficient solution for detecting evolving malware threats in cloud infrastructures.

RESEARCH TOPICS

Keywords

Malware Detection Cloud Infrastructures Convolutional Neural Networks Real-time Processing Feature Engineering

Downloads

Download data is not yet available.
SOURCES & SCHOLARSHIP

References

  1. Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.
  2. Dahl, G. E., Stokes, J. W., Deng, L., & Yu, D. (2013). Large-scale malware classification using random projections and neural networks. IEEE International Conference on Acoustics, Speech and Signal Processing, 3422–3426.
  3. Egele, M., Scholte, T., Kirda, E., & Kruegel, C. (2008). A survey on automated dynamic malware analysis solutions using virtualization. International Journal of Information Security, 10(1), 1–18.
  4. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  5. Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
  6. Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.
  7. Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Military Communications and Information Systems Conference, 1–6.
  8. Saxe, J., & Berlin, K. (2015). Deep neural network-based malware detection using two-dimensional binary program features. 10th International Conference on Malicious and Unwanted Software (MALWARE), 11–20.
  9. Zhao, Z., & Chen, L. (2019). Deep learning for real-time malware detection in cloud computing environments. IEEE Transactions on Services Computing, 12(2), 1–12.
  10. Rahman, M. M., Akhi, S. S., Hossain, S., Ayub, M. I., Siddique, M. T., Nath, A., ... & Hassan, M. M. (2024). EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY. The American Journal of Engineering and Technology, 6(12), 68-83.
  11. Das, P., Pervin, T., Bhattacharjee, B., Karim, M. R., Sultana, N., Khan, M. S., ... & Kamruzzaman, F. N. U. (2024). OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(12), 163-177.
  12. Hossain, M. N., Hossain, S., Nath, A., Nath, P. C., Ayub, M. I., Hassan, M. M., ... & Rasel, M. (2024). ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS. American Research Index Library, 23-35.
  13. Rishad, S. S. I., Shakil, F., Tisha, S. A., Afrin, S., Hassan, M. M., Choudhury, M. Z. M. E., & Rahman, N. (2025). LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS. American Research Index Library, 6-25.
  14. Uddin, A., Pabel, M. A. H., Alam, M. I., KAMRUZZAMAN, F., Haque, M. S. U., Hosen, M. M., ... & Ghosh, S. K. (2025). Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques. The American Journal of Management and Economics Innovations, 7(01), 5-20.
  15. Ahmed, M. P., Das, A. C., Akter, P., Mou, S. N., Tisha, S. A., Shakil, F., ... & Ahmed, A. (2024). HARNESSING MACHINE LEARNING MODELS FOR ACCURATE CUSTOMER LIFETIME VALUE PREDICTION: A COMPARATIVE STUDY IN MODERN BUSINESS ANALYTICS. American Research Index Library, 06-22.
  16. Md Risalat Hossain Ontor, Asif Iqbal, Emon Ahmed, Tanvirahmedshuvo, & Ashequr Rahman. (2024). LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS’ PERFORMANCE: A MACHINE LEARNING APPROACH. International Journal of Computer Science & Information System, 9(11), 45–56. https://doi.org/10.55640/ijcsis/Volume09Issue11-05
  17. Rahman, A., Iqbal, A., Ahmed, E., & Ontor, M. R. H. (2024). PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT. International journal of business and management sciences, 4(12), 18-32.
  18. Iqbal, A., Ahmed, E., Rahman, A., & Ontor, M. R. H. (2024). ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(11), 78-91.
  19. Nguyen, Q. G., Nguyen, L. H., Hosen, M. M., Rasel, M., Shorna, J. F., Mia, M. S., & Khan, S. I. (2025). Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction. The American Journal of Applied sciences, 7(01), 21-30.
  20. Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing,Management and Economics Journal, 4(12), 66-83.
  21. Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
CONTINUE EXPLORING

Most read articles by the same author(s)

Explore other published research by the authors of this article.

1 2 > >>